Attention mechanism, which enables network to focus on the most informative views, has been proven effective in many fields, such as image classification[1]}, image inpainting[2]}, medical image segmentation[3]}, etc. However, the mechanism hurts the object localization performance of WSOD because it focuses only on the most discriminative features. To address this issue, we propose a cascaded attentive dropout module (CADM) to inducing the network to learn less discriminative features for classification but meaningful features for object localization. Specifically, following channel attentive dropout, two complementary attentive branch are built and randomly selected to build spatial-attentive feature maps, where the one rewards the most discriminative features while the other punishes them via a designed attentive dropout strategy. A global context module (GCM), which uses sigmoid to enhance nonlinearity and perform feature fusion through element-wise multiplication and additions, also been introduced into the proposed network to obtain better global context information.
